Cross-modal similarity learning via pairs, preferences, and active supervision

Proceedings of the National Conference on Artificial Intelligence

Volumn 4, Issue , 2015, Pages 3203-3209

  Zhen, Yi ^a   Rai, Piyush ^b   Zha, Hongyuan ^a   Carin, Lawrence ^b  

^a GEORGIA INSTITUTE OF TECHNOLOGY   (United States)

^b DUKE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BINARY CODES; BINS;

ACTIVE LEARNING; BIPARTITE GRAPHS; CROSS-MODAL; LINK PREDICTION; PREFERENCE-BASED; PROBABILISTIC FRAMEWORK; SIMILARITY LEARNING; SIMILARITY SEARCH;

ARTIFICIAL INTELLIGENCE;

EID: 84960171692     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (25)

1. Bonilla, E. V.; Guo, S.; and Sanner, S. 2010. Gaussian process preference elicitation. In NIPS.
2. Bronstein, M. M.; Bronstein, A. M.; Michel, R; and Paragios, N. 2010. Data fusion through cross-modality metric learning using similarity-sensitive hashing. In CVPR.
3. Cortes, C, and Mohri, M. 2003. AUC optimization vs. error rate minimization. In NIPS.
4. Goriir, D.; Jakel, F.; and Rasmussen, C. E. 2006. A choice model with infinitely many latent features. In ICML.
5. Grauman, K., and Fergus, R. 2013. Learning binary hash codes for large-scale image search. In Machine Learning for Computer Vision. Springer. 49-87.
6. Griffiths, T. L., and Ghahramani, Z. 2011. The Indian buffet process: An introduction and review. JMLR 12:1185-1224.
7. Houlsby, N.; Hernandez-Lobato, J. M.; Huszar, F; and Ghahramani, Z. 2012. Collaborative gaussian processes for preference learning. In NIPS.
8. Jia, Y.; Salzmann, M; and Darrell, T. 2011. Learning crossmodality similarity for multinomial data. In ICCV.
9. Kuang, Z., and Wong, K. K. 2013. Relatively-paired space analysis. In BMVC.
10. Kumar, S., and Udupa, R. 2011. Learning hash functions for cross-view similarity search. In IJCAL
11. Li, X.; Lin, G.; Shen, C; Van den Hengel, A.; and Dick, A. 2013. Learning hash functions using column generation. In ICML.
12. Masci, J.; Bronstein, M. M.; Bronstein, A. M.; and Schmidhuber, J. 2013. Multimodal similarity-preserving hashing. TPAMI.
13. Mignon, A.; Jurie, F.; et al. 2012. CMML: A new metric learning approach for cross modal matching. In ACML.
14. Murray, I.; Adams, R. P.; and MacKay, D. J. C. 2010. Elliptical slice sampling. In AISTATS.
15. Ou, M.; Cui, P.; Wang, R; Wang, J.; Zhu, W.; and Yang, S. 2013. Comparing apples to oranges: A scalable solution with heterogeneous hashing. In KDD.
16. Quadrianto, N.; Sharmanska, V.; Austria, I.; Knowles, D. A.; and Ghahramani, Z. 2013. The supervised IBP: Neighbourhood preserving infinite latent feature models. In UAl
17. Rastegari, M.; Choi, J.; Fakhraei, S.; Daume, H.; and Davis, L. S. 2013. Predictable dual-view hashing. In ICML.
18. Teh, Y. W.; Goriir, D.; and Ghahramani, Z. 2007. Stickbreaking construction for the Indian buffet process. In AIS-TATS.
19. Wang, J.; Wang, J.; Yu, N.; and Li, S. 2013a. Order preserving hashing for approximate nearest neighbor search. In ACM MM.
20. Wang, J.; Liu, W.; Sun, A. X.; and Jiang, Y.-G. 2013b. Learning hash codes with listwise supervision. In ICCV.
21. Whang, J. J.; Rai, P.; and Dhillon, I. S. 2013. Stochastic blockmodel with cluster overlap, relevance selection, and similarity-based smoothing. In ICDM.
22. Yue, Y; Finley, T.; Radlinski, F; and Joachims, T. 2007. A support vector method for optimizing average precision. In SIGIR.
23. Zhai, D.; Chang, H.; Zhen, Y.; Liu, X.; Chen, X.; and Gao, W. 2013. Parametric local multimodal hashing for crossview similarity search. In IJCAl
24. Zhen, Y, and Yeung, D.-Y. 2012a. Co-regularized hashing for multimodal data. In NIPS.
25. Zhen, Y, and Yeung, D.-Y. 2012b. A probabilistic approach to multimodal hash function learning. In KDD.